From 35ae4a23298e274d0ee42d799ce181d52905153b Mon Sep 17 00:00:00 2001 From: pinb Date: Tue, 21 Nov 2023 22:08:54 +0900 Subject: [PATCH] update --- README.md | 353 ++++++++++++++++++++++++++++++++++++++++ autoencoder.ipynb | 2 +- images/autoencoder.png | Bin 180407 -> 47806 bytes images/autoencoder2.png | Bin 0 -> 251916 bytes 4 files changed, 354 insertions(+), 1 deletion(-) create mode 100644 images/autoencoder2.png diff --git a/README.md b/README.md index e69de29..4bcb96b 100644 --- a/README.md +++ b/README.md @@ -0,0 +1,353 @@ +--- +layout: default +title: 16. Autoencoder +subtitle: Deep Learning +--- +----- + +[PINBlog Gitea Repository](https://gitea.pinblog.codes/CBNU/16_Autoencoder) + +----- + +# AutoEncoder +- 산업인공지능학과 대학원 + 2022254026 + 김홍열 + + +--- + + +# **AutoEncoder 란** + +![autoencoder](./images/autoencoder.png) + +지도학습은 단순히 말해 입력 x와 정답 y 사이의 관계를 찾는 것이고, 비지도 학습은 정답이 없는 채로 x를 예측하는 것이다. + +즉 오토인코더에서는 입력도 x, 정답도 x로 설계된 신경망이다. + +단, 신경망은 범용근사자(univeral function approximator)로서 근사치를 출력하므로 x와 똑같은 출력을 내긴 힘들다. + +그러므로 오찻값에도 x를 얼마나 복원했는지를 뜻하는 복원오차, 혹은 정보손실값(reconstruction loss)이라는 용어를 사용한다. + + + + +# **AutoEncoder** 아키텍처의 주요 특징 + +![autoencoder](./images/autoencoder2.png) + +오토인코더는 입력과 출력의 크기는 같지만 중간으로 갈수록 신경망의 차원이 줄어든다. + +작은 차원으로 압축된 표현을 잠재변수(latent variable)라 하고, 간단히 z라고 한다. + +잠재 변수의 앞뒤를 구분하여 앞부분을 인코더(encoder), 뒷부분을 디코더(decoder)라고 한다. + +인코더는 정보를 받아 압축하고, 디코더는 압축된 표현을 풀어 입력을 복원하는 역할을 한다. + +오토인코더에서는 필연적으로 정보의 손실이 일어나지만 이는 중요한 정보만 남겨두는 일종의 데이터 가공이라고 볼 수 있다. + +이런 특징으로 인해 오토인코더는 주로 복잡한 비선형 데이터의 차원을 줄이는 용도로 쓰인다. + + + +# 데이터셋 **(Fashion-MNIST)** + +![fation-mnist](./images/fashionmnist.jpg) + +* Fashion-MNIST는 총 70,000개의 그레이스케일 이미지로 구성된다. +* 60,000개는 학습용 데이터, 10,000개는 테스트용 데이터 +* 데이터셋은 10가지 범주로 구성 ('티셔츠/탑', '바지', '풀오버', '드레스', '코트', '샌들', '셔츠', '스니커즈', '가방', '앵클 부츠' 등) +* 각 이미지는 28x28x1 크기 + + +--- + +### 예제 코드[¶]() + +
+Import +
+ +```python + +import torch +import torchvision +import torch.nn.functional as F +from torch import nn, optim +from torchvision import transforms, datasets + +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D #생성되는 이미지를 관찰하기 위함입니다. 3차원 플롯을 그리는 용도입니다. +from matplotlib import cm # 데이터포인트에 색상을 입히는 것에 사용됩니다. +import numpy as np + + +``` + +
+
+ +
+Hyper Parameters +
+ +```python + +# 하이퍼파라미터 준비 +EPOCH = 10 +BATCH_SIZE = 64 +USE_CUDA = torch.cuda.is_available() +DEVICE = torch.device("cuda" if USE_CUDA else "cpu") +print("Using Device:", DEVICE) + + +``` + +
+
+ + +--- + +### 예제 코드[¶]() + +
+Fashion-MNIST Dataset +
+ +```python + +# Fashion MNIST 데이터셋 불러오기 (학습데이터만 사용) +trainset = datasets.FashionMNIST( + root = './.data/', + train = True, + download = True, + transform = transforms.ToTensor() +) +train_loader = torch.utils.data.DataLoader( + dataset = trainset, + batch_size = BATCH_SIZE, + shuffle = True, + num_workers = 2 +) + +``` + +
+
+ +
+Result +
+ +```plaintext + +C:\Users\pinb\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\tqdm\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html + from .autonotebook import tqdm as notebook_tqdm +Using Device: cuda +Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz +Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./.data/FashionMNIST\raw\train-images-idx3-ubyte.gz +100%|██████████| 26421880/26421880 [00:06<00:00, 4076925.90it/s] +Extracting ./.data/FashionMNIST\raw\train-images-idx3-ubyte.gz to ./.data/FashionMNIST\raw + +Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz +Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./.data/FashionMNIST\raw\train-labels-idx1-ubyte.gz +100%|██████████| 29515/29515 [00:00<00:00, 118201.02it/s] +Extracting ./.data/FashionMNIST\raw\train-labels-idx1-ubyte.gz to ./.data/FashionMNIST\raw + +Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz +Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./.data/FashionMNIST\raw\t10k-images-idx3-ubyte.gz +100%|██████████| 4422102/4422102 [00:03<00:00, 1360040.16it/s] +Extracting ./.data/FashionMNIST\raw\t10k-images-idx3-ubyte.gz to ./.data/FashionMNIST\raw + +Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz +Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./.data/FashionMNIST\raw\t10k-labels-idx1-ubyte.gz +100%|██████████| 5148/5148 [00:00 +
+ + +--- + +### 예제 코드[¶]() + +
+AutoEncoder Network +
+ +```python + +# 오토인코더 모듈 정의 +class Autoencoder(nn.Module): + def __init__(self): + super(Autoencoder, self).__init__() + + #인코더는 간단한 신경망으로 분류모델처럼 생겼습니다. + self.encoder = nn.Sequential( # nn.Sequential을 사용해 encoder와 decoder 두 모듈로 묶어줍니다. + nn.Linear(28*28, 128), #차원을 28*28에서 점차 줄여나갑니다. + nn.ReLU(), + nn.Linear(128, 64), + nn.ReLU(), + nn.Linear(64, 12), + nn.ReLU(), + nn.Linear(12, 3), # 입력의 특징을 3차원으로 압축합니다 (출력값이 바로 잠재변수가 됩니다.) + ) + self.decoder = nn.Sequential( + nn.Linear(3, 12), #디코더는 차원을 점차 28*28로 복원합니다. + nn.ReLU(), + nn.Linear(12, 64), + nn.ReLU(), + nn.Linear(64, 128), + nn.ReLU(), + nn.Linear(128, 28*28), + nn.Sigmoid(), # 픽셀당 0과 1 사이로 값을 출력하는 sigmoid()함수를 추가합니다. + ) + + def forward(self, x): + encoded = self.encoder(x) # encoder는 encoded라는 잠재변수를 만들고 + decoded = self.decoder(encoded) # decoder를 통해 decoded라는 복원이미지를 만듭니다. + return encoded, decoded + + +``` + +
+
+ +
+Optimizer +
+ +```python + +autoencoder = Autoencoder().to(DEVICE) +optimizer = torch.optim.Adam(autoencoder.parameters(), lr=0.005) +# Adam()을 최적화함수로 사용합니다. Adam은 SGD의 변형함수이며 학습중인 기울기를 참고하여 학습 속도를 자동으로 변화시킵니다. +criterion = nn.MSELoss() #원본값과 디코더에서 나온 값의 차이를 계산하기 위해 평균제곱오차(Mean Squared Loss) 오차함수를 사용합니다. + + +``` + +
+
+ + + + +
+Data Normalize +
+ +```python + +# 원본 이미지를 시각화 하기 (첫번째 열) +view_data = trainset.data[:5].view(-1, 28*28) +# 복원이 어떻게 되는지 관찰하기 위해 5개의 이미지를 가져와 바로 넣어보겠습니다. +view_data = view_data.type(torch.FloatTensor)/255. +#픽셀의 색상값이 0~255이므로 모델이 인식하는 0부터 1사이의 값으로 만들기 위해 255로 나눠줍니다. + + +``` + +
+
+ +
+Train +
+ +```python + +# 학습하기 위한 함수 +def train(autoencoder, train_loader): + autoencoder.train() + for step, (x, label) in enumerate(train_loader): + x = x.view(-1, 28*28).to(DEVICE) + y = x.view(-1, 28*28).to(DEVICE) #x(입력)와 y(대상 레이블)모두 원본이미지(x)인 것을 주의해야 합니다. + label = label.to(DEVICE) + + encoded, decoded = autoencoder(x) + + loss = criterion(decoded, y) # decoded와 원본이미지(y) 사이의 평균제곱오차를 구합니다 + optimizer.zero_grad() #기울기에 대한 정보를 초기화합니다. + loss.backward() # 기울기를 구합니다. + optimizer.step() #최적화를 진행합니다. + + +``` + +
+
+ +
+Result +
+ +```python + +#학습하기 +for epoch in range(1, EPOCH+1): + train(autoencoder, train_loader) + + # 디코더에서 나온 이미지를 시각화 하기 + # 앞서 시각화를 위해 남겨둔 5개의 이미지를 한 이폭만큼 학습을 마친 모델에 넣어 복원이미지를 만듭니다. + test_x = view_data.to(DEVICE) + _, decoded_data = autoencoder(test_x) + + # 원본과 디코딩 결과 비교해보기 + f, a = plt.subplots(2, 5, figsize=(5, 2)) + print("[Epoch {}]".format(epoch)) + for i in range(5): + img = np.reshape(view_data.data.numpy()[i],(28, 28)) #파이토치 텐서를 넘파이로 변환합니다. + a[0][i].imshow(img, cmap='gray') + a[0][i].set_xticks(()); a[0][i].set_yticks(()) + + for i in range(5): + img = np.reshape(decoded_data.to("cpu").data.numpy()[i], (28, 28)) + # CUDA를 사용하면 모델 출력값이 GPU에 남아있으므로 .to("cpu") 함수로 일반메모리로 가져와 numpy행렬로 변환합니다. + # cpu를 사용할때에도 같은 코드를 사용해도 무방합니다. + a[1][i].imshow(img, cmap='gray') + a[1][i].set_xticks(()); a[1][i].set_yticks(()) + plt.show() + + +``` + +![fation-mnist](./images/output1.png.jpg) +![fation-mnist](./images/output2.png.jpg) +![fation-mnist](./images/output3.png.jpg) +![fation-mnist](./images/output10.png.jpg) + + +
+
+ + +# **AutoEncoder의 활용** + +* 차원 감소 ( Dimensionally Reduction) +인코더는 입력을 선형 및 비선형의 데이터로 차원을 줄이기 위하여 히든레이어로 인코딩한다 +* 추천 엔진 (Recommendation Engines) +* 이상탐지(Anommaly Detection) +오토인코더는 훈련의 일부로 재구성 오류 (Reconstruction error) 를 최소화하려 한다. 따라서 재구성 손실의 크기를 통하여 이상치를 탐지할 수 있다 +* 이미지 잡음제거 (Denoising images) +변질된 이미지가 원래 버전으로 복원이 가능하다 +* 이미지 인식 (Image Recognnition) +겹겹이 쌓인 오토인코더는 이미지의 다른 특성을 학습하여 이미지 인식에 사용된다 +* 이미지 생성 (Image generation) +오토인코더의 한 종류인 변형 오토인코더 (VAE, Variational Autoencoder)는 이미지 생성에 사용된다 + + +--- + +### 참고[¶]() + +- AutoEncoder - Google +- ChatGPT +- [Blog](https://velog.io/@jochedda/%EB%94%A5%EB%9F%AC%EB%8B%9D-Autoencoder-%EA%B0%9C%EB%85%90-%EB%B0%8F-%EC%A2%85%EB%A5%98) diff --git a/autoencoder.ipynb b/autoencoder.ipynb index f532d56..104438f 100644 --- a/autoencoder.ipynb +++ b/autoencoder.ipynb @@ -6,7 +6,7 @@ "source": [ "# AutoEncoder\n", "\n", - "![autoencoder](./images/autoencoder.png)\n", + "![autoencoder](./images/autoencoder2.png)\n", "\n", "지도학습은 단순히 말해 입력 x와 정답 y 사이의 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